Correlation Between Federated Bond and Federated Emerging
Can any of the company-specific risk be diversified away by investing in both Federated Bond and Federated Emerging at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Federated Bond and Federated Emerging into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Federated Bond Fund and Federated Emerging Market, you can compare the effects of market volatilities on Federated Bond and Federated Emerging and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Federated Bond with a short position of Federated Emerging. Check out your portfolio center. Please also check ongoing floating volatility patterns of Federated Bond and Federated Emerging.
Diversification Opportunities for Federated Bond and Federated Emerging
0.83 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Federated and Federated is 0.83. Overlapping area represents the amount of risk that can be diversified away by holding Federated Bond Fund and Federated Emerging Market in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Federated Emerging Market and Federated Bond is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Federated Bond Fund are associated (or correlated) with Federated Emerging. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Federated Emerging Market has no effect on the direction of Federated Bond i.e., Federated Bond and Federated Emerging go up and down completely randomly.
Pair Corralation between Federated Bond and Federated Emerging
Assuming the 90 days horizon Federated Bond Fund is expected to generate 0.91 times more return on investment than Federated Emerging. However, Federated Bond Fund is 1.1 times less risky than Federated Emerging. It trades about 0.14 of its potential returns per unit of risk. Federated Emerging Market is currently generating about 0.12 per unit of risk. If you would invest 831.00 in Federated Bond Fund on September 3, 2024 and sell it today you would earn a total of 7.00 from holding Federated Bond Fund or generate 0.84% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 100.0% |
Values | Daily Returns |
Federated Bond Fund vs. Federated Emerging Market
Performance |
Timeline |
Federated Bond |
Federated Emerging Market |
Federated Bond and Federated Emerging Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Federated Bond and Federated Emerging
The main advantage of trading using opposite Federated Bond and Federated Emerging positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Federated Bond position performs unexpectedly, Federated Emerging can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Federated Emerging will offset losses from the drop in Federated Emerging's long position.Federated Bond vs. Tax Managed Mid Small | Federated Bond vs. Small Cap Value | Federated Bond vs. Champlain Small | Federated Bond vs. Rbc Small Cap |
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Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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